phi2-lora-quantized-distilabel-intel-orca-dpo-pairs
This model is a fine-tuned version of microsoft/phi-2 on distilabel-intel-orca-dpo-pairs. The full training notebook can be found here.
It achieves the following results on the evaluation set:
- Loss: 0.4537
- Rewards/chosen: -0.0837
- Rewards/rejected: -1.2628
- Rewards/accuracies: 0.8301
- Rewards/margins: 1.1791
- Logps/rejected: -224.8409
- Logps/chosen: -203.2228
- Logits/rejected: 0.4773
- Logits/chosen: 0.3062
Model description
The adapter was fine-tuned on a Google Colab A100 GPU using DPO and the distilabel-intel-orca-dpo-pairs. In order to scale LoRa approached for LLMs, I recommend looking at predibase/lorax.
You can play around with the model shown below. We load the LoRa adapter and bits_n_bytes config (only when CUDA is available).
import torch
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig
)
from peft import PeftModel
# template used for fine-tune
# template = """\
# Instruct: {instruction}\n
# Output: {response}"""
if torch.cuda.is_available():
device = torch.device("cuda")
print(f"Using {torch.cuda.get_device_name(0)}")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_compute_dtype='float16',
bnb_4bit_use_double_quant=False,
)
elif torch.backends.mps.is_available():
device = torch.device("mps")
bnb_config = None
else:
device = torch.device("cpu")
bnb_config = None
print("No GPU available, using CPU instead.")
config = PeftConfig.from_pretrained("davidberenstein1957/phi2-lora-quantized-distilabel-intel-orca-dpo-pairs")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float16, quantization_config=bnb_config)
model = PeftModel.from_pretrained(model, "davidberenstein1957/phi2-lora-quantized-distilabel-intel-orca-dpo-pairs").to(device)
prompt = "Instruct: What is the capital of France? \nOutput:""
inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs)
text = tokenizer.batch_decode(outputs)[0]
Intended uses & limitations
This is a LoRa adapter fine-tine for phi-2 and not a full fine-tune of the model. Additionally, I did not spend time updating parameters.
Training and evaluation data
The adapter was fine-tuned on a Google Colab A100 GPU using DPO and the distilabel-intel-orca-dpo-pairs. The full training notebook can be found here. Underneath, there are some configs for the adapter and the trainer.
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.5,
r=32,
target_modules=['k_proj', 'q_proj', 'v_proj', 'fc1', 'fc2'],
bias="none",
task_type="CAUSAL_LM",
)
training_arguments = TrainingArguments(
output_dir=f"./{model_name}",
evaluation_strategy="steps",
do_eval=True,
optim="paged_adamw_8bit",
per_device_train_batch_size=2,
gradient_accumulation_steps=16,
per_device_eval_batch_size=2,
log_level="debug",
save_steps=20,
logging_steps=20,
learning_rate=1e-5,
eval_steps=20,
num_train_epochs=1, # Modified for tutorial purposes
max_steps=100,
warmup_steps=20,
lr_scheduler_type="linear",
)
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|
0.6853 | 0.06 | 20 | 0.6701 | 0.0133 | -0.0368 | 0.6905 | 0.0501 | -212.5803 | -202.2522 | 0.3853 | 0.2532 |
0.6312 | 0.12 | 40 | 0.5884 | 0.0422 | -0.2208 | 0.8138 | 0.2630 | -214.4207 | -201.9638 | 0.4254 | 0.2816 |
0.547 | 0.19 | 60 | 0.5146 | 0.0172 | -0.5786 | 0.8278 | 0.5958 | -217.9983 | -202.2132 | 0.4699 | 0.3110 |
0.4388 | 0.25 | 80 | 0.4893 | -0.0808 | -1.0789 | 0.8293 | 0.9981 | -223.0014 | -203.1934 | 0.5158 | 0.3396 |
0.4871 | 0.31 | 100 | 0.4818 | -0.1298 | -1.2346 | 0.8297 | 1.1048 | -224.5586 | -203.6837 | 0.5133 | 0.3340 |
0.4863 | 0.37 | 120 | 0.4723 | -0.1230 | -1.1718 | 0.8301 | 1.0488 | -223.9305 | -203.6159 | 0.4910 | 0.3167 |
0.4578 | 0.44 | 140 | 0.4666 | -0.1257 | -1.1772 | 0.8301 | 1.0515 | -223.9844 | -203.6428 | 0.4795 | 0.3078 |
0.4587 | 0.5 | 160 | 0.4625 | -0.0746 | -1.1272 | 0.8301 | 1.0526 | -223.4841 | -203.1310 | 0.4857 | 0.3139 |
0.4688 | 0.56 | 180 | 0.4595 | -0.0584 | -1.1194 | 0.8297 | 1.0610 | -223.4062 | -202.9692 | 0.4890 | 0.3171 |
0.4189 | 0.62 | 200 | 0.4579 | -0.0666 | -1.1647 | 0.8297 | 1.0982 | -223.8598 | -203.0511 | 0.4858 | 0.3138 |
0.4392 | 0.68 | 220 | 0.4564 | -0.0697 | -1.1915 | 0.8301 | 1.1219 | -224.1278 | -203.0823 | 0.4824 | 0.3110 |
0.4659 | 0.75 | 240 | 0.4554 | -0.0826 | -1.2245 | 0.8301 | 1.1419 | -224.4574 | -203.2112 | 0.4761 | 0.3052 |
0.4075 | 0.81 | 260 | 0.4544 | -0.0823 | -1.2328 | 0.8301 | 1.1504 | -224.5403 | -203.2089 | 0.4749 | 0.3044 |
0.4015 | 0.87 | 280 | 0.4543 | -0.0833 | -1.2590 | 0.8301 | 1.1757 | -224.8026 | -203.2188 | 0.4779 | 0.3067 |
0.4365 | 0.93 | 300 | 0.4539 | -0.0846 | -1.2658 | 0.8301 | 1.1812 | -224.8702 | -203.2313 | 0.4780 | 0.3067 |
0.4589 | 1.0 | 320 | 0.4537 | -0.0837 | -1.2628 | 0.8301 | 1.1791 | -224.8409 | -203.2228 | 0.4773 | 0.3062 |
Framework versions
- PEFT 0.7.1
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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Model tree for argilla/phi2-lora-distilabel-intel-orca-dpo-pairs
Base model
microsoft/phi-2